用局部补丁扩散模型，统一修复热成像的模糊、噪点和低分辨率问题。
TDiff: Thermal Plug-And-Play Prior with Patch-Based Diffusion
- 将热图像切块，用扩散模型学习局部退化先验。
- 在模拟与真实数据上实现去噪、超分、去模糊三任务统一修复。
- 适合需要多任务热成像增强的研究者与工业应用
低成本热成像相机获取的图像常存在低分辨率、固定模式噪声及其他局部退化问题，且现有热成像数据集在规模与多样性上均有限。为此，我们提出一种基于补丁的扩散框架（TDiff），通过在小块热图像上训练，利用退化的局部特性进行建模。该方法通过去噪重叠补丁并使用平滑空间加权融合，实现全分辨率图像重建。据我们所知，这是首个针对多种任务（去噪、超分辨率、去模糊）建模热图像修复学习先验的补丁基扩散框架。在模拟与真实热数据上的实验表明，该方法性能优越，可作为统一的修复流水线。
原文摘要 · Abstract (English)
Thermal images from low-cost cameras often suffer from low resolution, fixed pattern noise, and other localized degradations. Available datasets for thermal imaging are also limited in both size and diversity. To address these challenges, we propose a patch-based diffusion framework (TDiff) that leverages the local nature of these distortions by training on small thermal patches. In this approach, full-resolution images are restored by denoising overlapping patches and blending them using smooth spatial windowing. To our knowledge, this is the first patch-based diffusion framework that models a learned prior for thermal image restoration across multiple tasks. Experiments on denoising, super-resolution, and deblurring demonstrate strong results on both simulated and real thermal data, establishing our method as a unified restoration pipeline.
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